The global smart grid technology market is growing due to increasing demand for grid optimization and efficiency. Growth in electricity generation by independent power producers (IPPs) and nonutility generators (NUGs), as well greater electrical output using renewable resources, result in increased product sales, since these operations need to be appropriately equipped to deliver power to the electric grid. Furthermore, efforts to modernize the electric grid and to improve efficiency and reliability, support market advances via the incorporation of “smart” technologies that provide a number of advantages, but also cost considerably more than conventional equipment.
Smart meters are generally digital meters that send energy usage information via radio frequency electromagnetic radiation to a utility company. They have evolved from traditional analog meters to digital electronic devices that are capable of tracking and recording a customer's energy usage, as well as communicate with the energy supplier. Smart meters help reduce meter reading costs, inefficiencies in billing, disconnection and re-connection costs to the corporations, utilities, as well as consumers.
The smart grid vision is generally targeting fully automated power grids where reliable and efficient operation of every system element, node, and customers are utilized in order to optimize the energy value chain. Achieving this vision in distribution system imposes self-healing abilities to autonomously take corrective actions, operate the system to the best possible state and perform basic functions without violating any constraints. Self-healing systems may tend to perform continuous monitoring and analysis during three different modes of system operation, which may include: under normal operation; during impending faults; and during system faults.
During impending faults, self-healing systems may aim to prevent failures instead of mitigating them. They may be capable of detecting and localizing impending faults and anticipating their influence to trigger a full-scale event. However, faults may sometimes be unavoidable. Hence, self-healing systems quickly isolate faulty sections, to avoid spread over of disruption, and enable rapid restoration and dynamic updating of protection settings based on actual system configuration.
The success of implementing self-healing algorithms in distribution systems may be dependent on complete integration and interaction of different functions within distribution management systems. Different data sources may be interfaced to share different data models and serve different functions. Some challenges when developing such systems may include, for instance, the need to significantly increase real-time and non-real-time measurements, adopting modularity to implement system functions, and the ability of managing high data sets. Moreover, anticipating impending faults in primary substations and other scattered distribution system equipment may be challenging. Early stages of failure modes of some equipment, such as switchgears, transformers, voltage regulators, and capacitor banks are developed over time with blurred symptoms that may not be noticed until devices fail. In addition, cracked transformer bushings, degradation in line insulators, bad terminal connections, and thermal stresses are usually the major causes to hot spots, partial discharge (PD), sparks, or electric arcs.
PD and arcs are common symptoms of impending faults, however, their transient and intermittent nature disable protection relays to detect until flashover with devastating impact is developed. Unless assets drop offline, distribution systems operators are often unaware of the developing conditions leading to equipment failures. Furthermore, future distribution systems are subject to great uncertainty due to penetration of distributed generation and electric vehicles as well as consumer response to real-time pricing and rewarding policies. Anticipating an impending fault in such stressed systems, which operate using complex protection, is a difficult task.
There is a need therefore, for a system and/or method that is capable of monitoring and diagnosing equipment health. There is a further need for a system and/or method that is capable of continuously monitoring distributed energy system operation, detect and localize impending faults, and isolates faulty sections of system equipment.